OpenRaiser/Intern-Atlas huggingface dataset:
Primal-dual splitting scheme with backtracking for handling with epigraphic constraint and sparse analysis regularization
Venue: arXiv cs.IT 2020
Year: 2020

The convergence of many proximal algorithms involving a gradient descent relies on its Lipschitz constant. To avoid computing it, backtracking rules can be used. While such a rule has already been designed for the forward-backward algorithm (FBwB), this scheme is not flexible enough when a non-differentiable penalization with a linear operator is added to a constraint. In this work we propose a backtracking rule for the primal-dual scheme (PDwB), and evaluate its performance for the epigraphical constrained high dynamical reconstruction in high contrast polarimetric imaging, under TV penalization.
TaBooN -- Boolean Network Synthesis Based on Tabu Search
Venue: arXiv cs.AI 2020
Year: 2020

Recent developments in Omics-technologies revolutionized the investigation of biology by producing molecular data in multiple dimensions and scale. This breakthrough in biology raises the crucial issue of their interpretation based on modelling. In this undertaking, network provides a suitable framework for modelling the interactions between molecules. Basically a Biological network is composed of nodes referring to the components such as genes or proteins, and the edges/arcs formalizing interactions between them. The evolution of the interactions is then modelled by the definition of a dynamical system. Among the different categories of network, the Boolean network offers a reliable qualitative framework for the modelling. Automatically synthesizing a Boolean network from experimental data therefore remains a necessary but challenging issue. In this study, we present taboon, an original work-flow for synthesizing Boolean Networks from biological data. The methodology uses the data in the form of Boolean profiles for inferring all the potential local formula inference. They combine to form the model space from which the most truthful model with regards to biological knowledge and experiments must be found. In the taboon work-flow the selection of the fittest model is achieved by a Tabu-search algorithm. taboon is an automated method for Boolean Network inference from experimental data that can also assist to evaluate and optimize the dynamic behaviour of the biological networks providing a reliable platform for further modelling and predictions.
IAI MovieBot: A Conversational Movie Recommender System
Venue: arXiv cs.IR 2020
Year: 2020

Conversational recommender systems support users in accomplishing recommendation-related goals via multi-turn conversations. To better model dynamically changing user preferences and provide the community with a reusable development framework, we introduce IAI MovieBot, a conversational recommender system for movies. It features a task-specific dialogue flow, a multi-modal chat interface, and an effective way to deal with dynamically changing user preferences. The system is made available open source and is operated as a channel on Telegram.
Computer Program Decomposition and Dynamic/Behavioral Modeling
Venue: arXiv cs.SE 2020
Year: 2020

Decomposition, statically dividing a program into multiple units, is a common programming technique for realizing parallelism and refining programs. The decomposition of a sequential program into components is tedious, due to the limitations of program analysis and because sequential programs frequently employ inherently sequential algorithms. This paper contributes to this area of study by proposing a diagrammatic methodology to decompose a sequential program. The methodology involves visualizing the program in terms of a conceptual model called the thinging machine (TM) model. The TM diagram-based model establishes three levels of representation (1) a static description; (2) a dynamic representation; and (3) a behavioral model. The decomposition is performed in the last phase of modeling, according to the streams of events. This method is contrasted with formal decomposition specifications and compared with the typical decomposition of a C++ program. The results point to the viability of using TM for decomposing programs.
Development of Adaptive Frame Reservation Scheme and Naive Persistent State Co-Located Coexistence Controller
Venue: arXiv cs.NI 2020
Year: 2020

Future broadband networks need to provide high capacity at low cost with increased revenue through enhanced services. WiMAX came up as one of the leading technologies, however, the 2.3 GHz and 2.5 GHz frequency bands allocated create two serious coexistence issues with the adjacent 2.4 GHz ISM band. First problem is to address radio interfaces that are located on two independent platforms and still possess the potential for mutual interference owing to close proximity to each other. The Adaptive Frame Reservation Scheme presented here extends the CTS frame reservation signaling defined in 802.11 specifications to a demand based and adaptive scheme. Second issue is to address the coexistence problem in multi-radio platforms where two or more radios are co-located, creating an even worse interference scenario. This can be managed by hardware signaling that can be made available between radio interfaces through OS control. The development of a smart Co-located Coexistence Controller is explored which continuously receives transmission, reception and sleep requests from attached interfaces and in return grant permissions.
Sequenced Route Query with Semantic Hierarchy
Venue: arXiv cs.DB 2020
Year: 2020

The trip planning query searches for preferred routes starting from a given point through multiple Point-of-Interests (PoI) that match user requirements. Although previous studies have investigated trip planning queries, they lack flexibility for finding routes because all of them output routes that strictly match user requirements. We study trip planning queries that output multiple routes in a flexible manner. We propose a new type of query called skyline sequenced route (SkySR) query, which searches for all preferred sequenced routes to users by extending the shortest route search with the semantic similarity of PoIs in the route. Flexibility is achieved by the {\it semantic hierarchy} of the PoI category. We propose an efficient algorithm for the SkySR query, bulk SkySR algorithm that simultaneously searches for sequenced routes and prunes unnecessary routes effectively. Experimental evaluations show that the proposed approach significantly outperforms the existing approaches in terms of response time (up to four orders of magnitude). Moreover, we develop a prototype service that uses the SkySR query, and conduct a user test to evaluate its usefulness.
Randomness Concerns When Deploying Differential Privacy
Venue: arXiv cs.CR 2020
Year: 2020

The U.S. Census Bureau is using differential privacy (DP) to protect confidential respondent data collected for the 2020 Decennial Census of Population & Housing. The Census Bureau's DP system is implemented in the Disclosure Avoidance System (DAS) and requires a source of random numbers. We estimate that the 2020 Census will require roughly 90TB of random bytes to protect the person and household tables. Although there are critical differences between cryptography and DP, they have similar requirements for randomness. We review the history of random number generation on deterministic computers, including von Neumann's "middle-square" method, Mersenne Twister (MT19937) (previously the default NumPy random number generator, which we conclude is unacceptable for use in production privacy-preserving systems), and the Linux /dev/urandom device. We also review hardware random number generator schemes, including the use of so-called "Lava Lamps" and the Intel Secure Key RDRAND instruction. We finally present our plan for generating random bits in the Amazon Web Services (AWS) environment using AES-CTR-DRBG seeded by mixing bits from /dev/urandom and the Intel Secure Key RDSEED instruction, a compromise of our desire to rely on a trusted hardware implementation, the unease of our external reviewers in trusting a hardware-only implementation, and the need to generate so many random bits.
Machine Intelligence for Outcome Predictions of Trauma Patients During Emergency Department Care
Venue: arXiv cs.LG 2020
Year: 2020

Trauma mortality results from a multitude of non-linear dependent risk factors including patient demographics, injury characteristics, medical care provided, and characteristics of medical facilities; yet traditional approach attempted to capture these relationships using rigid regression models. We hypothesized that a transfer learning based machine learning algorithm could deeply understand a trauma patient's condition and accurately identify individuals at high risk for mortality without relying on restrictive regression model criteria. Anonymous patient visit data were obtained from years 2007-2014 of the National Trauma Data Bank. Patients with incomplete vitals, unknown outcome, or missing demographics data were excluded. All patient visits occurred in U.S. hospitals, and of the 2,007,485 encounters that were retrospectively examined, 8,198 resulted in mortality (0.4%). The machine intelligence model was evaluated on its sensitivity, specificity, positive and negative predictive value, and Matthews Correlation Coefficient. Our model achieved similar performance in age-specific comparison models and generalized well when applied to all ages simultaneously. While testing for confounding factors, we discovered that excluding fall-related injuries boosted performance for adult trauma patients; however, it reduced performance for children. The machine intelligence model described here demonstrates similar performance to contemporary machine intelligence models without requiring restrictive regression model criteria or extensive medical expertise.
Technical Report: Gone in 20 Seconds -- Overview of a Password Vulnerability in Siemens HMIs
Venue: arXiv cs.CR 2020
Year: 2020

Siemens produce a range of industrial human machine interface (HMI) screens which allow operators to both view information about and control physical processes. For scenarios where an operator cannot physically access the screen, Siemens provide the SM@rtServer features on HMIs, which when activated provides remote access either through their own Sm@rtClient application, or through third party VNC client software. Through analysing this server, we discovered a lack of protection against brute-force password attacks on basic devices. On advanced devices which include a brute-force protection mechanism, we discovered an attacker strategy that is able to evade the mechanism allowing for unlimited password guess attempts with minimal effect on the guess rate. This vulnerability has been assigned two CVEs - CVE-2020-15786 and CVE-2020-157867. In this report, we provide an overview of this vulnerability, discuss the impact of a successful exploitation and propose mitigations to provide protection against this vulnerability. This report accompanies a demo presented at CPSIoTSec 2020.
Tangent Space Based Alternating Projections for Nonnegative Low Rank Matrix Approximation
Venue: arXiv cs.LG 2020
Year: 2020

In this paper, we develop a new alternating projection method to compute nonnegative low rank matrix approximation for nonnegative matrices. In the nonnegative low rank matrix approximation method, the projection onto the manifold of fixed rank matrices can be expensive as the singular value decomposition is required. We propose to use the tangent space of the point in the manifold to approximate the projection onto the manifold in order to reduce the computational cost. We show that the sequence generated by the alternating projections onto the tangent spaces of the fixed rank matrices manifold and the nonnegative matrix manifold, converge linearly to a point in the intersection of the two manifolds where the convergent point is sufficiently close to optimal solutions. This convergence result based inexact projection onto the manifold is new and is not studied in the literature. Numerical examples in data clustering, pattern recognition and hyperspectral data analysis are given to demonstrate that the performance of the proposed method is better than that of nonnegative matrix factorization methods in terms of computational time and accuracy.
Profiling Developers Through the Lens of Technical Debt
Venue: arXiv cs.SE 2020
Year: 2020

Context: Technical Debt needs to be managed to avoid disastrous consequences, and investigating developers' habits concerning technical debt management is invaluable information in software development. Objective: This study aims to characterize how developers manage technical debt based on the code smells they induce and the refactorings they apply. Method: We mined a publicly-available Technical Debt dataset for Git commit information, code smells, coding violations, and refactoring activities for each developer of a selected project. Results: By combining this information, we profile developers to recognize prolific coders, highlight activities that discriminate among developer roles (reviewer, lead, architect), and estimate coding maturity and technical debt tolerance.
View-consistent 4D Light Field Depth Estimation
Venue: arXiv cs.CV 2020
Year: 2020

We propose a method to compute depth maps for every sub-aperture image in a light field in a view consistent way. Previous light field depth estimation methods typically estimate a depth map only for the central sub-aperture view, and struggle with view consistent estimation. Our method precisely defines depth edges via EPIs, then we diffuse these edges spatially within the central view. These depth estimates are then propagated to all other views in an occlusion-aware way. Finally, disoccluded regions are completed by diffusion in EPI space. Our method runs efficiently with respect to both other classical and deep learning-based approaches, and achieves competitive quantitative metrics and qualitative performance on both synthetic and real-world light fields
Central Yup'ik and Machine Translation of Low-Resource Polysynthetic Languages
Venue: arXiv cs.CL 2020
Year: 2020

Machine translation tools do not yet exist for the Yup'ik language, a polysynthetic language spoken by around 8,000 people who live primarily in Southwest Alaska. We compiled a parallel text corpus for Yup'ik and English and developed a morphological parser for Yup'ik based on grammar rules. We trained a seq2seq neural machine translation model with attention to translate Yup'ik input into English. We then compared the influence of different tokenization methods, namely rule-based, unsupervised (byte pair encoding), and unsupervised morphological (Morfessor) parsing, on BLEU score accuracy for Yup'ik to English translation. We find that using tokenized input increases the translation accuracy compared to that of unparsed input. Although overall Morfessor did best with a vocabulary size of 30k, our first experiments show that BPE performed best with a reduced vocabulary size.
Deep Metric Learning Meets Deep Clustering: An Novel Unsupervised Approach for Feature Embedding
Venue: arXiv cs.CV 2020
Year: 2020

Unsupervised Deep Distance Metric Learning (UDML) aims to learn sample similarities in the embedding space from an unlabeled dataset. Traditional UDML methods usually use the triplet loss or pairwise loss which requires the mining of positive and negative samples w.r.t. anchor data points. This is, however, challenging in an unsupervised setting as the label information is not available. In this paper, we propose a new UDML method that overcomes that challenge. In particular, we propose to use a deep clustering loss to learn centroids, i.e., pseudo labels, that represent semantic classes. During learning, these centroids are also used to reconstruct the input samples. It hence ensures the representativeness of centroids - each centroid represents visually similar samples. Therefore, the centroids give information about positive (visually similar) and negative (visually dissimilar) samples. Based on pseudo labels, we propose a novel unsupervised metric loss which enforces the positive concentration and negative separation of samples in the embedding space. Experimental results on benchmarking datasets show that the proposed approach outperforms other UDML methods.
Understanding the Dynamics of Drivers' Locations for Passengers Pickup Performance: A Case Study
Venue: arXiv cs.CY 2020
Year: 2020

With the emergence of e-hailing taxi services, a growing number of scholars have attempted to analyze the taxi trips data to gain insights from drivers' and passengers' flow patterns and understand different dynamics of urban public transportation. Existing studies are limited to passengers' location analysis e.g., pick-up and drop-off points, in the context of maximizing the profits or better managing the resources for service providers. Moreover, taxi drivers' locations at the time of pick-up requests and their pickup performance in the spatial-temporal domain have not been explored. In this paper, we analyze drivers' and passengers' locations at the time of booking request in the context of drivers' pick-up performances. To facilitate our analysis, we implement a modified and extended version of a co-clustering technique, called sco-iVAT, to obtain useful clusters and co-clusters from big relational data, derived from booking records of Grab ride-hailing service in Singapore. We also explored the possibility of predicting timely pickup for a given booking request, without using entire trajectories data. Finally, we devised two scoring mechanisms to compute pickup performance score for all driver candidates for a booking request. These scores could be integrated into a booking assignment model to prioritize top-performing drivers for passenger pickups.
Mobile Apps for Foot Measurement: A Scoping Review
Venue: arXiv cs.CY 2020
Year: 2020

With the proliferation of smart phone, a major growth in the use of apps related to the health category, specifically those concerned with foot health can be observed. Although new, these apps are being used practically for scanning feet with an aim to providing accurate information about various properties of the human foot. With the availability of many 'foot scanning and measuring apps' in the app stores, the need for an evaluation system for such apps can be deemed necessary as little information regarding the evidence-based quality of these apps is available. To characterize the assessment of measurement techniques and essential software quality characteristics of mobile foot measuring apps, and determine their effectiveness for potential use as commercial professional tools for foot care health professionals such as pedorthists, podiatrists, orthotists and so on, to assist in measuring foot for custom shoes, and for individuals to enhance the awareness of foot health and hygiene and prevention of foot-related problems. An electronic search across Android and iOS app stores was conducted between July 2020 and August 2020 for apps related to foot measurement. Mobile apps with stated goals of foot measurement and general foot health were identified and selected by three independent raters and discrepancies regarding the selected apps were resolved via a fourth rater. Evaluation inferences found all apps failing to meet even half of the measurement-specific criteria required for the proper manufacturing of custom-made footwear. 23% (6/26) of apps were found to utilize either external scanners or advanced algorithms to reconstruct 3D models of user foot that can possibly be used for ordering custom-made footwear and medical casts for fitting irregular foot sizes and shapes. Overall, current apps for foot measurement do not follow any specific guidelines for measurement purposes.
Optimizing BLE-Like Neighbor Discovery
Venue: arXiv cs.NI 2020
Year: 2020

Neighbor discovery (ND) protocols are used for establishing a first contact between multiple wireless devices. The energy consumption and discovery latency of this procedure are determined by the parametrization of the protocol. In most existing protocols, reception and transmission are temporally coupled. Such schemes are referred to as \textit{slotted}, for which the problem of finding optimized parametrizations has been studied thoroughly in the literature. However, slotted approaches are not efficient in applications in which new devices join the network gradually and only the joining devices and a master node need to run the ND protocol simultaneously. For example, this is typically the case in IoT scenarios or Bluetooth Low Energy (BLE) piconets. Here, protocols in which packets are transmitted with periodic intervals (PI) can achieve significantly lower worst-case latencies than slotted ones. For this class of protocols, optimal parameter values remain unknown. To address this, we propose an optimization framework for PI-based BLE-like protocols, which translates any specified duty-cycle (and therefore energy budget) into a set of optimized parameter values. We show that the parametrizations resulting from one variant of our proposed scheme are optimal when one receiver discovers one transmitter, and no other parametrization or ND protocol - neither slotted nor slotless - can guarantee lower discovery latencies for a given duty-cycle in this scenario. Since the resulting protocol utilizes the channel more aggressively than other ND protocols, beacons will collide more frequently. Hence, due to collisions, the rate of successful discoveries gracefully decreases for larger numbers of devices discovering each other simultaneously. We also propose a scheme for configuring the BLE protocol (and not just BLE-\textit{like} protocols).
Real-World Snapshots vs. Theory: Questioning the t-Probing Security Model
Venue: arXiv cs.CR 2020
Year: 2020

Due to its sound theoretical basis and practical efficiency, masking has become the most prominent countermeasure to protect cryptographic implementations against physical side-channel attacks (SCAs). The core idea of masking is to randomly split every sensitive intermediate variable during computation into at least t+1 shares, where t denotes the maximum number of shares that are allowed to be observed by an adversary without learning any sensitive information. In other words, it is assumed that the adversary is bounded either by the possessed number of probes (e.g., microprobe needles) or by the order of statistical analyses while conducting higher-order SCA attacks (e.g., differential power analysis). Such bounded models are employed to prove the SCA security of the corresponding implementations. Consequently, it is believed that given a sufficiently large number of shares, the vast majority of known SCA attacks are mitigated. In this work, we present a novel laser-assisted SCA technique, called Laser Logic State Imaging (LLSI), which offers an unlimited number of contactless probes, and therefore, violates the probing security model assumption. This technique enables us to take snapshots of hardware implementations, i.e., extract the logical state of all registers at any arbitrary clock cycle with a single measurement. To validate this, we mount our attack on masked AES hardware implementations and practically demonstrate the extraction of the full-length key in two different scenarios. First, we assume that the location of the registers (key and/or state) is known, and hence, their content can be directly read by a single snapshot. Second, we consider an implementation with unknown register locations, where we make use of multiple snapshots and a SAT solver to reveal the secrets.